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Open AccessArticle

Conceptual Encoding and Advanced Management of Leonardo da Vinci’s Mona Lisa: Preliminary Results

1
Dipartimento di Ingegneria Informatica, Modellistica, Elettronica e Sistemistica, University of Calabria, 87036 Rende (CS), Italy
2
STIH Laboratory, Sorbonne University, 75005 Paris, France
*
Author to whom correspondence should be addressed.
Information 2019, 10(10), 321; https://doi.org/10.3390/info10100321
Received: 24 September 2019 / Revised: 8 October 2019 / Accepted: 12 October 2019 / Published: 17 October 2019
This paper describes a preliminary experiment concerning the use of advanced Artificial Intelligence/Knowledge Representation techniques to improve the present formalization/digitization procedures of Cultural Heritage assets—with reference, in particular, to all types of Cultural Heritage “iconographic” entities. In this context, in agreement with the recent proposal to characterize the digital description of Cultural Heritage items making use of the notion of “Cultural Heritage Digital Twin”, we are mainly concerned with the possibility to consider not only the external, “physical”, aspects of these iconographic items but also the “message” they convey in a more or less explicit way. For our experiment, some aspects of the Mona Lisa painting by Leonardo da Vinci have been formalized, along with their context, making use of NKRL, the “Narrative Knowledge Representation Language”. NKRL is, in reality, both a Knowledge Representation language and a full Computer Science environment, used to represent/manage in an advanced way “narrative” (in the widest meaning of this word) information. The initial results of the experiment are described in the paper, along with some thoughts about their possible interest and developments. View Full-Text
Keywords: ontology; semantic web; pattern recognition; Leonardo da Vinci; Mona Lisa ontology; semantic web; pattern recognition; Leonardo da Vinci; Mona Lisa
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Amelio, A.; Zarri, G.P. Conceptual Encoding and Advanced Management of Leonardo da Vinci’s Mona Lisa: Preliminary Results. Information 2019, 10, 321.

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